import datetime
date_time_str = '2018-06-29 08:15:27.243860'
date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S.%f')
print('Date:', date_time_obj.date())
print('Time:', date_time_obj.time())
print('Date-time:', date_time_obj)

>>> import datetime
>>> datetime.datetime.strptime('24052010', "%d%m%Y").date()
datetime.date(2010, 5, 24)

from datetime import datetime
datetime_object = datetime.strptime('Jun 1 2005 1:33PM', '%b %d %Y %I:%M%p')

# import the datetime module
import datetime
  
# datetime in string format for may 25 1999
input = '2021/05/25'
  
# format
format = '%Y/%m/%d'
  
# convert from string format to datetime format
datetime = datetime.datetime.strptime(input, format)
  
# get the date from the datetime using date() 
# function
print(datetime.date())

from datetime import datetime
my_date_string = "Mar 11 2011 11:31AM"
datetime_object = datetime.strptime(my_date_string, '%b %d %Y %I:%M%p')
print(type(datetime_object))
print(datetime_object)

from dateutil.parser import parse
parse('31, March 31, 2010, 10:51pm')

 Load libraries
import pandas as pd
from datetime import timedelta
# Loading dataset and creating duration column
url = 'https://drive.google.com/uc?id=1YV5bKobzYxVAWyB7VlxNH6dmfP4tHBui'
df = pd.read_csv(url, parse_dates = ['pickup_datetime', 'dropoff_datetime', 'dropoff_calculated'])
df["duration"] = pd.to_timedelta(df["duration"])
# Task 1 - filter to only rides with negative durations
df_neg = df[___["___"] < ___(___)]
# Task 2 - iterate over df_neg rows to find inconsistencies
count = 0
for i, row in df_neg.___(): # Compare minutes of dropoff_datetime and dropoff_calculated if row["___"].___ != row["___"].minute: # Print these two columns print(___[["dropoff_datetime", "dropoff_calculated"]]) # Task 3 - count number of rows having hour greater-equal than 12 if row["___"].___ >= ___: count ___
print(f"There are {count} rows in df_neg having hour greater-equal than 12.")